Digital Image Processing Chapter 6: Color Image Processing 6 July 2005

Spectrum of White Light 1666 Sir Isaac Newton, 24 year old, discovered white light spectrum. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Electromagnetic Spectrum Visible light wavelength: from around 400 to 700 nm 1. For an achromatic (monochrome) light source, there is only 1 attribute to describe the quality: intensity 2. For a chromatic light source, there are 3 attributes to describe the quality: Radiance = total amount of energy flow from a light source (Watts) Luminance = amount of energy received by an observer (lumens) Brightness = intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Sensitivity of Cones in the Human Eye 6-7 millions cones in a human eye - 65% sensitive to Red light - 33% sensitive to Green light - 2 % sensitive to Blue light Primary colors: Defined CIE in 1931 Red = 700 nm Green = 546.1nm Blue = 435.8 nm CIE = Commission Internationale de l’Eclairage (The International Commission on Illumination) (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Primary and Secondary Colors

Primary and Secondary Colors (cont.) Additive primary colors: RGB use in the case of light sources such as color monitors RGB add together to get white Subtractive primary colors: CMY use in the case of pigments in printing devices White subtracted by CMY to get Black (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Characterization Hue: dominant color corresponding to a dominant wavelength of mixture light wave Saturation: Relative purity or amount of white light mixed with a hue (inversely proportional to amount of white light added) Brightness: Intensity Hue Saturation Chromaticity amount of red (X), green (Y) and blue (Z) to form any particular color is called tristimulus.

CIE Chromaticity Diagram Trichromatic coefficients: y Points on the boundary are fully saturated colors x (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Gamut of Color Monitors and Printing Devices (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

RGB Color Model Purpose of color models: to facilitate the specification of colors in some standard RGB color models: based on cartesian coordinate system (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

RGB Color Cube R = 8 bits Color depth 24 bits G = 8 bits B = 8 bits Color depth 24 bits = 16777216 colors Hidden faces of the cube (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

RGB Color Model (cont.) Red fixed at 127 (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Safe RGB Colors Safe RGB colors: a subset of RGB colors. There are 216 colors common in most operating systems. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

RGB Safe-color Cube The RGB Cube is divided into 6 intervals on each axis to achieve the total 63 = 216 common colors. However, for 8 bit color representation, there are the total 256 colors. Therefore, the remaining 40 colors are left to OS. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

CMY and CMYK Color Models C = Cyan M = Magenta Y = Yellow K = Black

HSI Color Model RGB, CMY models are not good for human interpreting Hue: Dominant color Saturation: Relative purity (inversely proportional to amount of white light added) Intensity: Brightness Color carrying information (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Relationship Between RGB and HSI Color Models (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Hue and Saturation on Color Planes 1. A dot is the plane is an arbitrary color 2. Hue is an angle from a red axis. 3. Saturation is a distance to the point.

HSI Color Model (cont.) Intensity is given by a position on the vertical axis.

HSI Color Model Intensity is given by a position on the vertical axis.

Example: HSI Components of RGB Cube Hue Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Converting Colors from RGB to HSI

Converting Colors from HSI to RGB RG sector: GB sector: BR sector:

Example: HSI Components of RGB Colors Image Hue Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Example: Manipulating HSI Components RGB Image Hue Hue Saturation RGB Image Saturation Intensity Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Processing There are 2 types of color image processes 1. Pseudocolor image process: Assigning colors to gray values based on a specific criterion. Gray scale images to be processed may be a single image or multiple images such as multispectral images 2. Full color image process: The process to manipulate real color images such as color photographs.

Pseudocolor Image Processing Pseudo color = false color : In some case there is no “color” concept for a gray scale image but we can assign “false” colors to an image. Why we need to assign colors to gray scale image? Answer: Human can distinguish different colors better than different shades of gray. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Intensity Slicing or Density Slicing Formula: C1 = Color No. 1 C2 = Color No. 2 Color T C2 C1 T L-1 Intensity A gray scale image viewed as a 3D surface.

Intensity Slicing Example An X-ray image of a weld with cracks After assigning a yellow color to pixels with value 255 and a blue color to all other pixels. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Multi Level Intensity Slicing Ck = Color No. k lk = Threshold level k Color Ck Ck-1 C3 C2 C1 l1 l2 l3 lk-1 lk L-1 Intensity

Multi Level Intensity Slicing Example Ck = Color No. k lk = Threshold level k An X-ray image of the Picker Thyroid Phantom. After density slicing into 8 colors (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Coding Example A unique color is assigned to each intensity value. Gray-scale image of average monthly rainfall. Color map Color coded image South America region (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Gray Level to Color Transformation Assigning colors to gray levels based on specific mapping functions Red component Gray scale image Green component Blue component (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Gray Level to Color Transformation Example An X-ray image of a garment bag with a simulated explosive device An X-ray image of a garment bag (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Transformations Color coded images

Gray Level to Color Transformation Example An X-ray image of a garment bag with a simulated explosive device An X-ray image of a garment bag Transformations (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Color coded images

Pseudocolor Coding Used in the case where there are many monochrome images such as multispectral satellite images. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Biomass and shoreline mapping Pseudocolor Coding Example Visible blue = 0.45-0.52 mm Max water penetration Visible green = 0.52-0.60 mm Measuring plant Color composite images 1 2 Red = Green = Blue = Red = Green = Blue = 1 1 3 4 2 2 3 4 Visible red = 0.63-0.69 mm Plant discrimination Near infrared = 0.76-0.90 mm Biomass and shoreline mapping Washington D.C. area (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Pseudocolor Coding Example Psuedocolor rendition of Jupiter moon Io Yellow areas = older sulfur deposits. Red areas = material ejected from active volcanoes. A close-up (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Basics of Full-Color Image Processing 2 Methods: 1. Per-color-component processing: process each component separately. 2. Vector processing: treat each pixel as a vector to be processed. Example of per-color-component processing: smoothing an image By smoothing each RGB component separately. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Example: Full-Color Image and Variouis Color Space Components CMYK components RGB components HSI components (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Transformation Use to transform colors to colors. Formulation: f(x,y) = input color image, g(x,y) = output color image T = operation on f over a spatial neighborhood of (x,y) When only data at one pixel is used in the transformation, we can express the transformation as: i= 1, 2, …, n Where ri = color component of f(x,y) si = color component of g(x,y) For RGB images, n = 3

Example: Color Transformation Formula for RGB: k = 0.7 Formula for HSI: Formula for CMY: I H,S These 3 transformations give the same results. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Complements Color complement replaces each color with its opposite color in the color circle of the Hue component. This operation is analogous to image negative in a gray scale image. Color circle (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Complement Transformation Example (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Slicing Transformation We can perform “slicing” in color space: if the color of each pixel is far from a desired color more than threshold distance, we set that color to some specific color such as gray, otherwise we keep the original color unchanged. Set to gray Keep the original color i= 1, 2, …, n or Set to gray Keep the original color i= 1, 2, …, n

Color Slicing Transformation Example After color slicing Original image (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Tonal Correction Examples In these examples, only brightness and contrast are adjusted while keeping color unchanged. This can be done by using the same transformation for all RGB components. Contrast enhancement Power law transformations (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Balancing Correction Examples Color imbalance: primary color components in white area are not balance. We can measure these components by using a color spectrometer. Color balancing can be performed by adjusting color components separately as seen in this slide. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Histogram Equalization of a Full-Color Image Histogram equalization of a color image can be performed by adjusting color intensity uniformly while leaving color unchanged. The HSI model is suitable for histogram equalization where only Intensity (I) component is equalized. where r and s are intensity components of input and output color image.

Histogram Equalization of a Full-Color Image Original image After histogram equalization After increasing saturation component (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Smoothing 2 Methods: Per-color-plane method: for RGB, CMY color models Smooth each color plane using moving averaging and the combine back to RGB Smooth only Intensity component of a HSI image while leaving H and S unmodified. Note: 2 methods are not equivalent.

Color Image Smoothing Example (cont.) Red Color image Green Blue (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Smoothing Example (cont.) HSI Components Hue Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Smoothing Example (cont.) Smooth all RGB components Smooth only I component of HSI (faster) (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Smoothing Example (cont.) Difference between smoothed results from 2 methods in the previous slide. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Sharpening We can do in the same manner as color image smoothing: 1. Per-color-plane method for RGB,CMY images 2. Sharpening only I component of a HSI image Sharpening all RGB components Sharpening only I component of HSI

Color Image Sharpening Example (cont.) Difference between sharpened results from 2 methods in the previous slide. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Segmentation 2 Methods: Segmented in HSI color space: A thresholding function based on color information in H and S Components. We rarely use I component for color image segmentation. Segmentation in RGB vector space: A thresholding function based on distance in a color vector space. (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Segmentation in HSI Color Space Color image Hue 1 2 3 4 Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Binary thresholding of S component Color Segmentation in HSI Color Space (cont.) Binary thresholding of S component with T = 10% Product of and 2 5 5 6 Red pixels 7 8 (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Histogram of 6 Segmentation of red color pixels

Color Segmentation in HSI Color Space (cont.) Color image Segmented results of red pixels (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Segmentation in RGB Vector Space (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. 1. Each point with (R,G,B) coordinate in the vector space represents one color. 2. Segmentation is based on distance thresholding in a vector space cT = color to be segmented. D(u,v) = distance function c(x,y) = RGB vector at pixel (x,y).

Example: Segmentation in RGB Vector Space Color image Reference color cT to be segmented Results of segmentation in RGB vector space with Threshold value T = 1.25 times the SD of R,G,B values In the box (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Gradient of a Color Image Since gradient is define only for a scalar image, there is no concept of gradient for a color image. We can’t compute gradient of each color component and combine the results to get the gradient of a color image. Red Green Blue We see 2 objects. We see 4 objects. Edges (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Gradient of a Color Image (cont.) One way to compute the maximum rate of change of a color image which is close to the meaning of gradient is to use the following formula: Gradient computed in RGB color space:

Gradient of a Color Image Example 2 Original image Obtained using the formula in the previous slide 3 Sum of gradients of each color component Difference between 2 and 3 2 3 (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Gradient of a Color Image Example Red Green Blue Gradients of each color component (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Noise in Color Images Noise can corrupt each color component independently. AWGN sh2=800 AWGN sh2=800 AWGN sh2=800 Noise is less noticeable in a color image (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Noise in Color Images Hue Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Noise in Color Images Salt & pepper noise in Green component Hue Saturation Intensity (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.

Color Image Compression Original image JPEG2000 File After lossy compression with ratio 230:1 (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.